Covid plots for family
April 13, 2020
Background
Notes and format last updated Apr 9, 2020
This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be the day prior.
- The page starts by showing growth rates. I’ve moved down the raw numbers to later in the page because you can see them everywhere, and it’s sometimes hard to understand trends looking at them. Growth rates seem to me to be more important for understanding our progress.
- For the U.S. and each of our states, we show two growth rate charts: confirmed cases (total and new) and deaths (total and new). Each chart has two lines: a growth-rate in the rolling three-day total of cases (or deaths) and a growth rate in a rolling three-day average of new cases (or deaths).
- For total cases (or deaths), the charts show the growth rate in total cases (or deaths) over the previous three days for each respective date. For example, a three-day growth rate of 100% for total cases means that total cases are doubling every three days. This rate can’t go below zero (because total cases (or deaths) can never go down), but we want it to get as close to zero as possible.
- For new cases (or deaths), the charts show the daily growth rate in the average number of new cases (or deaths) for the previous three days. This rate can go below zero because we’ll eventually start to see consistently less new cases (or deaths) each day. When the new case (or death) growth rate is consistently below zero, it means we’re on the other side of the “apex.”
- For the U.S. and each of our states, we show two growth rate charts: confirmed cases (total and new) and deaths (total and new). Each chart has two lines: a growth-rate in the rolling three-day total of cases (or deaths) and a growth rate in a rolling three-day average of new cases (or deaths).
- The page then shows state and county rankings normalized for population (number of cases or deaths per million residents).
- For states, it shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths. In addition to the tables, the trends for the top states are plotted over time.
- For counties, it shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths. The table also shows the top counties in the country for perspecive. And there is then a plot showing our counties’ percentiles over time.
- There is then a long section with raw counts for the U.S., our states, and our counties for each of these categories:
- Total confirmed cases: This is the cumulative total of cases reported.
- New confirmed cases: This is the number of new cases reported on each respective date.
- Total deaths: This is the cumulative total of deaths reported.
- New deaths: This is the number of new deaths reported on each respective date.
- Rolling three-day death growth rate: This is the same as described for total and new cases above, but for total and new deaths.
Finally, the last section compares data from states that have and have not enacted state-wide stay-at-home orders, trying to see if there are any effects of the orders. It’s a work in progress, and there are more extensive notes in that section.
Growth rates
Confirmed cases (total and new)
U.S.
agg.cases.US <- state.raw[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.US
agg.cases.US[, total_cases_prev3 := temp$total_cases[match(agg.cases.US$date - 3, temp$date)]]
agg.cases.US[, total_cases_rtgrowth := total_cases/total_cases_prev3 - 1]
agg.cases.US[, new_cases_prev3 := total_cases - total_cases_prev3]
temp <- agg.cases.US
agg.cases.US[, new_cases_prev3_last := temp$new_cases_prev3[match(agg.cases.US$date - 1, temp$date)]]
agg.cases.US[, new_cases_rtgrowth := new_cases_prev3/new_cases_prev3_last - 1]
melt.use <- melt(agg.cases.US[, c("date", "total_cases_rtgrowth", "new_cases_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_cases_rtgrowth", "Total Cases", "New Cases")]
agg.cases.growth.US.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID cases",
subtitle = "United States",
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.cases.growth.US.plot
Our states
use.state <- "Washington"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.state
agg.cases.state[, total_cases_prev3 := temp$total_cases[match(agg.cases.state$date - 3, temp$date)]]
agg.cases.state[, total_cases_rtgrowth := total_cases/total_cases_prev3 - 1]
agg.cases.state[, new_cases_prev3 := total_cases - total_cases_prev3]
temp <- agg.cases.state
agg.cases.state[, new_cases_prev3_last := temp$new_cases_prev3[match(agg.cases.state$date - 1, temp$date)]]
agg.cases.state[, new_cases_rtgrowth := new_cases_prev3/new_cases_prev3_last - 1]
melt.use <- melt(agg.cases.state[, c("date", "total_cases_rtgrowth", "new_cases_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_cases_rtgrowth", "Total Cases", "New Cases")]
agg.cases.growth.state.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID cases",
subtitle = use.state,
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.cases.growth.state.plot
use.state <- "California"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.state
agg.cases.state[, total_cases_prev3 := temp$total_cases[match(agg.cases.state$date - 3, temp$date)]]
agg.cases.state[, total_cases_rtgrowth := total_cases/total_cases_prev3 - 1]
agg.cases.state[, new_cases_prev3 := total_cases - total_cases_prev3]
temp <- agg.cases.state
agg.cases.state[, new_cases_prev3_last := temp$new_cases_prev3[match(agg.cases.state$date - 1, temp$date)]]
agg.cases.state[, new_cases_rtgrowth := new_cases_prev3/new_cases_prev3_last - 1]
melt.use <- melt(agg.cases.state[, c("date", "total_cases_rtgrowth", "new_cases_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_cases_rtgrowth", "Total Cases", "New Cases")]
agg.cases.growth.state.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID cases",
subtitle = use.state,
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.cases.growth.state.plot
use.state <- "South Carolina"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.state
agg.cases.state[, total_cases_prev3 := temp$total_cases[match(agg.cases.state$date - 3, temp$date)]]
agg.cases.state[, total_cases_rtgrowth := total_cases/total_cases_prev3 - 1]
agg.cases.state[, new_cases_prev3 := total_cases - total_cases_prev3]
temp <- agg.cases.state
agg.cases.state[, new_cases_prev3_last := temp$new_cases_prev3[match(agg.cases.state$date - 1, temp$date)]]
agg.cases.state[, new_cases_rtgrowth := new_cases_prev3/new_cases_prev3_last - 1]
melt.use <- melt(agg.cases.state[, c("date", "total_cases_rtgrowth", "new_cases_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_cases_rtgrowth", "Total Cases", "New Cases")]
agg.cases.growth.state.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID cases",
subtitle = use.state,
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.cases.growth.state.plot
use.state <- "Tennessee"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.state
agg.cases.state[, total_cases_prev3 := temp$total_cases[match(agg.cases.state$date - 3, temp$date)]]
agg.cases.state[, total_cases_rtgrowth := total_cases/total_cases_prev3 - 1]
agg.cases.state[, new_cases_prev3 := total_cases - total_cases_prev3]
temp <- agg.cases.state
agg.cases.state[, new_cases_prev3_last := temp$new_cases_prev3[match(agg.cases.state$date - 1, temp$date)]]
agg.cases.state[, new_cases_rtgrowth := new_cases_prev3/new_cases_prev3_last - 1]
melt.use <- melt(agg.cases.state[, c("date", "total_cases_rtgrowth", "new_cases_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_cases_rtgrowth", "Total Cases", "New Cases")]
agg.cases.growth.state.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID cases",
subtitle = use.state,
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.cases.growth.state.plot
Deaths (total and new)
U.S.
agg.deaths.US <- state.raw[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.US
agg.deaths.US[, total_deaths_prev3 := temp$total_deaths[match(agg.deaths.US$date - 3, temp$date)]]
agg.deaths.US[, total_deaths_rtgrowth := total_deaths/total_deaths_prev3 - 1]
agg.deaths.US[, new_deaths_prev3 := total_deaths - total_deaths_prev3]
temp <- agg.deaths.US
agg.deaths.US[, new_deaths_prev3_last := temp$new_deaths_prev3[match(agg.deaths.US$date - 1, temp$date)]]
agg.deaths.US[, new_deaths_rtgrowth := new_deaths_prev3/new_deaths_prev3_last - 1]
melt.use <- melt(agg.deaths.US[, c("date", "total_deaths_rtgrowth", "new_deaths_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_deaths_rtgrowth", "Total Deaths", "New Deaths")]
agg.deaths.growth.US.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID deaths",
subtitle = "United States",
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.deaths.growth.US.plot
Our states
use.state <- "Washington"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- deaths.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.state
agg.deaths.state[, total_deaths_prev3 := temp$total_deaths[match(agg.deaths.state$date - 3, temp$date)]]
agg.deaths.state[, total_deaths_rtgrowth := total_deaths/total_deaths_prev3 - 1]
agg.deaths.state[, new_deaths_prev3 := total_deaths - total_deaths_prev3]
temp <- agg.deaths.state
agg.deaths.state[, new_deaths_prev3_last := temp$new_deaths_prev3[match(agg.deaths.state$date - 1, temp$date)]]
agg.deaths.state[, new_deaths_rtgrowth := new_deaths_prev3/new_deaths_prev3_last - 1]
melt.use <- melt(agg.deaths.state[, c("date", "total_deaths_rtgrowth", "new_deaths_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_deaths_rtgrowth", "Total Deaths", "New Deaths")]
agg.deaths.growth.state.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID deaths",
subtitle = use.state,
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.deaths.growth.state.plot
use.state <- "California"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- deaths.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.state
agg.deaths.state[, total_deaths_prev3 := temp$total_deaths[match(agg.deaths.state$date - 3, temp$date)]]
agg.deaths.state[, total_deaths_rtgrowth := total_deaths/total_deaths_prev3 - 1]
agg.deaths.state[, new_deaths_prev3 := total_deaths - total_deaths_prev3]
temp <- agg.deaths.state
agg.deaths.state[, new_deaths_prev3_last := temp$new_deaths_prev3[match(agg.deaths.state$date - 1, temp$date)]]
agg.deaths.state[, new_deaths_rtgrowth := new_deaths_prev3/new_deaths_prev3_last - 1]
melt.use <- melt(agg.deaths.state[, c("date", "total_deaths_rtgrowth", "new_deaths_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_deaths_rtgrowth", "Total Deaths", "New Deaths")]
agg.deaths.growth.state.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID deaths",
subtitle = use.state,
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.deaths.growth.state.plot
use.state <- "South Carolina"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- deaths.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.state
agg.deaths.state[, total_deaths_prev3 := temp$total_deaths[match(agg.deaths.state$date - 3, temp$date)]]
agg.deaths.state[, total_deaths_rtgrowth := total_deaths/total_deaths_prev3 - 1]
agg.deaths.state[, new_deaths_prev3 := total_deaths - total_deaths_prev3]
temp <- agg.deaths.state
agg.deaths.state[, new_deaths_prev3_last := temp$new_deaths_prev3[match(agg.deaths.state$date - 1, temp$date)]]
agg.deaths.state[, new_deaths_rtgrowth := new_deaths_prev3/new_deaths_prev3_last - 1]
melt.use <- melt(agg.deaths.state[, c("date", "total_deaths_rtgrowth", "new_deaths_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_deaths_rtgrowth", "Total Deaths", "New Deaths")]
agg.deaths.growth.state.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID deaths",
subtitle = use.state,
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.deaths.growth.state.plot
use.state <- "Tennessee"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- deaths.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.state
agg.deaths.state[, total_deaths_prev3 := temp$total_deaths[match(agg.deaths.state$date - 3, temp$date)]]
agg.deaths.state[, total_deaths_rtgrowth := total_deaths/total_deaths_prev3 - 1]
agg.deaths.state[, new_deaths_prev3 := total_deaths - total_deaths_prev3]
temp <- agg.deaths.state
agg.deaths.state[, new_deaths_prev3_last := temp$new_deaths_prev3[match(agg.deaths.state$date - 1, temp$date)]]
agg.deaths.state[, new_deaths_rtgrowth := new_deaths_prev3/new_deaths_prev3_last - 1]
melt.use <- melt(agg.deaths.state[, c("date", "total_deaths_rtgrowth", "new_deaths_rtgrowth")], id.vars = "date")
melt.use[, variable := ifelse(variable %in% "total_deaths_rtgrowth", "Total Deaths", "New Deaths")]
agg.deaths.growth.state.plot <- ggplot(melt.use[date >= "2020-03-16"], aes(x = date, y = value, color = variable)) +
geom_line(aes(group = variable)) +
labs(x = "Date",
y = "Rate of growth (rolling 3-day window)",
title = "Rolling 3-day rate of growth: Confirmed COVID deaths",
subtitle = use.state,
color = "Metric") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits =
c(ifelse(min(melt.use$value) > 0, 0, min(melt.use$value)), max(melt.use$value * 1.1)))
agg.deaths.growth.state.plot
By population rankings
States
Total confirmed cases
Table of total confirmed cases per million residents (all 50 states)
state.per.cap <- merge(state.raw, stay_dates, by = "state", all.x = T)
state.per.cap[, casesPerMillion := as.integer(cases/population * 1000000)]
date.cut <- max(state.per.cap$date)
state.per.cap.agg <- state.per.cap[date == date.cut]
state.per.cap.agg <- state.per.cap.agg[order(-casesPerMillion), c("state", "casesPerMillion")]
state.per.cap.agg[, casesPerMillion := comma(casesPerMillion)]
state.per.cap.agg## state casesPerMillion
## 1: New York 9,699
## 2: New Jersey 6,963
## 3: Louisiana 4,430
## 4: Massachusetts 3,696
## 5: Connecticut 3,375
## 6: District of Columbia 2,656
## 7: Rhode Island 2,515
## 8: Michigan 2,452
## 9: Pennsylvania 1,793
## 10: Delaware 1,668
## 11: Illinois 1,645
## 12: Washington 1,367
## 13: Maryland 1,360
## 14: Colorado 1,268
## 15: Indiana 1,177
## 16: Vermont 1,165
## 17: Georgia 1,139
## 18: Mississippi 934
## 19: Nevada 928
## 20: Florida 925
## 21: South Dakota 825
## 22: Idaho 797
## 23: Tennessee 753
## 24: Alabama 730
## 25: New Hampshire 724
## 26: Utah 718
## 27: Missouri 677
## 28: South Carolina 644
## 29: Virginia 617
## 30: New Mexico 593
## 31: California 590
## 32: Wisconsin 573
## 33: Ohio 564
## 34: Iowa 502
## 35: Oklahoma 497
## 36: Arizona 486
## 37: Texas 475
## 38: Maine 470
## 39: Wyoming 466
## 40: Kansas 459
## 41: Kentucky 439
## 42: North Carolina 430
## 43: Arkansas 424
## 44: Nebraska 423
## 45: North Dakota 404
## 46: Alaska 369
## 47: Montana 362
## 48: Oregon 362
## 49: Hawaii 351
## 50: West Virginia 343
## 51: Minnesota 287
## 52: Puerto Rico 280
## 53: American Samoa <NA>
## 54: Guam <NA>
## 55: Northern Mariana Islands <NA>
## 56: Virgin Islands <NA>
## state casesPerMillionstate.per.cap.agg[, row := .I]
top.ten <- state.per.cap.agg[row < 11, "state"]
state.per.cap.series <- state.per.cap[state %in% top.ten$state]
state.per.cap.plot <- ggplot(state.per.cap.series[date >= "2020-03-01"], aes(x = date, y = casesPerMillion, colour = state)) +
geom_line(aes(group = state)) +
labs(x = "Date",
y = "Total confirmed COVID cases per million residents",
title = "Total confirmed COVID cases per million residents",
subtitle = paste0("States with 10 highest counts as of ", format(date.cut, "%B %d, %Y"))) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
state.per.cap.plot
New confirmed cases
Table of new cases per million residents: rolling 3-day total (all 50 states)
state.per.cap <- merge(state.raw, stay_dates, by = "state", all.x = T)
date.cut <- max(state.per.cap$date)
date.min <- max(state.per.cap$date) - 3
state.per.cap.agg <- state.per.cap[date == date.cut]
temp <- state.per.cap[date == date.min]
state.per.cap.agg[, casesPrev3 := temp$cases[match(state.per.cap.agg$state, temp$state)]]
state.per.cap.agg[, newCasesPrev3 := (cases - casesPrev3)]
state.per.cap.agg[, casesNewPerMillion := as.integer(newCasesPrev3/population * 1000000)]
state.per.cap.agg <- state.per.cap.agg[order(-casesNewPerMillion), c("state", "casesNewPerMillion")]
state.per.cap.agg[, casesNewPerMillion := comma(casesNewPerMillion)]
state.per.cap.agg## state casesNewPerMillion
## 1: New York 1,478
## 2: New Jersey 1,218
## 3: Massachusetts 947
## 4: Rhode Island 885
## 5: Connecticut 631
## 6: District of Columbia 498
## 7: Louisiana 497
## 8: Delaware 427
## 9: Pennsylvania 357
## 10: Illinois 349
## 11: Maryland 337
## 12: South Dakota 319
## 13: Michigan 312
## 14: Indiana 234
## 15: Colorado 191
## 16: Mississippi 175
## 17: Vermont 158
## 18: Alabama 151
## 19: Virginia 144
## 20: Florida 142
## 21: Nevada 131
## 22: New Hampshire 122
## 23: New Mexico 122
## 24: Nebraska 116
## 25: Georgia 114
## 26: Washington 105
## 27: South Carolina 102
## 28: Missouri 101
## 29: Iowa 100
## 30: Tennessee 100
## 31: Utah 100
## 32: Kentucky 98
## 33: Ohio 93
## 34: North Carolina 82
## 35: Texas 82
## 36: California 79
## 37: Wisconsin 78
## 38: Kansas 76
## 39: Oklahoma 72
## 40: Arizona 71
## 41: Minnesota 67
## 42: Puerto Rico 67
## 43: Maine 54
## 44: Wyoming 53
## 45: North Dakota 51
## 46: Alaska 50
## 47: West Virginia 50
## 48: Oregon 48
## 49: Arkansas 44
## 50: Hawaii 40
## 51: Idaho 40
## 52: Montana 30
## 53: American Samoa <NA>
## 54: Guam <NA>
## 55: Northern Mariana Islands <NA>
## 56: Virgin Islands <NA>
## state casesNewPerMillion
Total deaths
Table of total deaths per million residents (all 50 states)
state.per.cap <- merge(state.raw, stay_dates, by = "state", all.x = T)
state.per.cap[, deathsPerMillion := as.integer(deaths/population * 1000000)]
date.cut <- max(state.per.cap$date)
state.per.cap.agg <- state.per.cap[date == date.cut]
state.per.cap.agg <- state.per.cap.agg[order(-deathsPerMillion), c("state", "deathsPerMillion")]
state.per.cap.agg[, deathsPerMillion := comma(deathsPerMillion)]
state.per.cap.agg## state deathsPerMillion
## 1: New York 482
## 2: New Jersey 264
## 3: Louisiana 180
## 4: Connecticut 155
## 5: Michigan 148
## 6: Massachusetts 109
## 7: District of Columbia 70
## 8: Washington 67
## 9: Rhode Island 59
## 10: Illinois 57
## 11: Colorado 50
## 12: Indiana 50
## 13: Vermont 43
## 14: Georgia 41
## 15: Pennsylvania 41
## 16: Maryland 39
## 17: Nevada 36
## 18: Delaware 35
## 19: Mississippi 32
## 20: Oklahoma 24
## 21: Wisconsin 24
## 22: Florida 21
## 23: Kentucky 21
## 24: Ohio 21
## 25: Kansas 19
## 26: Alabama 18
## 27: Missouri 18
## 28: California 17
## 29: Arizona 16
## 30: New Hampshire 16
## 31: Virginia 16
## 32: Idaho 15
## 33: South Carolina 15
## 34: Tennessee 15
## 35: Maine 14
## 36: Puerto Rico 13
## 37: Iowa 12
## 38: Minnesota 12
## 39: New Mexico 12
## 40: Oregon 12
## 41: North Dakota 10
## 42: Nebraska 9
## 43: Texas 9
## 44: Alaska 8
## 45: Arkansas 8
## 46: North Carolina 8
## 47: Hawaii 6
## 48: South Dakota 6
## 49: Montana 5
## 50: Utah 5
## 51: West Virginia 4
## 52: Wyoming 0
## 53: American Samoa <NA>
## 54: Guam <NA>
## 55: Northern Mariana Islands <NA>
## 56: Virgin Islands <NA>
## state deathsPerMillionstate.per.cap.agg[, row := .I]
top.ten <- state.per.cap.agg[row < 11, "state"]
state.per.cap.series <- state.per.cap[state %in% top.ten$state]
state.per.cap.plot <- ggplot(state.per.cap.series[date >= "2020-03-01"], aes(x = date, y = deathsPerMillion, colour = state)) +
geom_line(aes(group = state)) +
labs(x = "Date",
y = "Total COVID-related deaths per million residents",
title = "Total COVID-related deaths per million residents",
subtitle = paste0("States with 10 highest counts as of ", format(date.cut, "%B %d, %Y"))) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
state.per.cap.plot
New deaths
Table of new deaths per million residents: rolling 3-day total (all 50 states)
state.per.cap <- merge(state.raw, stay_dates, by = "state", all.x = T)
date.cut <- max(state.per.cap$date)
date.min <- max(state.per.cap$date) - 3
state.per.cap.agg <- state.per.cap[date == date.cut]
temp <- state.per.cap[date == date.min]
state.per.cap.agg[, deathsPrev3 := temp$deaths[match(state.per.cap.agg$state, temp$state)]]
state.per.cap.agg[, newDeathsPrev3 := (deaths - deathsPrev3)]
state.per.cap.agg[, deathsNewPerMillion := as.integer(newDeathsPrev3/population * 1000000)]
state.per.cap.agg <- state.per.cap.agg[order(-deathsNewPerMillion), c("state", "deathsNewPerMillion")]
state.per.cap.agg[, deathsNewPerMillion := comma(deathsNewPerMillion)]
state.per.cap.agg## state deathsNewPerMillion
## 1: New York 119
## 2: New Jersey 73
## 3: Connecticut 48
## 4: Michigan 41
## 5: Massachusetts 36
## 6: Louisiana 29
## 7: District of Columbia 25
## 8: Rhode Island 18
## 9: Maryland 16
## 10: Illinois 15
## 11: Indiana 14
## 12: Pennsylvania 14
## 13: Delaware 12
## 14: Colorado 10
## 15: Nevada 10
## 16: Washington 7
## 17: Mississippi 6
## 18: Vermont 6
## 19: Florida 4
## 20: Iowa 4
## 21: Kansas 4
## 22: New Mexico 4
## 23: Oklahoma 4
## 24: Wisconsin 4
## 25: Alabama 3
## 26: Arizona 3
## 27: California 3
## 28: Kentucky 3
## 29: Minnesota 3
## 30: Missouri 3
## 31: North Dakota 3
## 32: Ohio 3
## 33: Puerto Rico 3
## 34: Virginia 3
## 35: Georgia 2
## 36: Hawaii 2
## 37: Maine 2
## 38: South Carolina 2
## 39: Texas 2
## 40: Alaska 1
## 41: Arkansas 1
## 42: Idaho 1
## 43: Nebraska 1
## 44: New Hampshire 1
## 45: North Carolina 1
## 46: Oregon 1
## 47: Tennessee 1
## 48: Utah 1
## 49: West Virginia 1
## 50: Montana 0
## 51: Wyoming 0
## 52: South Dakota -1
## 53: American Samoa <NA>
## 54: Guam <NA>
## 55: Northern Mariana Islands <NA>
## 56: Virgin Islands <NA>
## state deathsNewPerMillionstate.per.cap.agg[, row := .I]
top.ten <- state.per.cap.agg[row < 11, "state"]
state.per.cap.series <- state.per.cap[state %in% top.ten$state]
temp <- state.per.cap.series[, c("state", "date", "deaths")]
setnames(temp, "deaths", "deathsPrev3")
state.per.cap.series[, datePrev3 := date - 3]
state.per.cap.series <- merge(state.per.cap.series, temp, all.x = T, by.x = c("state", "datePrev3"), by.y = c("state", "date"))
state.per.cap.series[, newDeathsPrev3 := (deaths - deathsPrev3)]
state.per.cap.series[, deathsNewPerMillion := as.integer(newDeathsPrev3/population * 1000000)]
state.per.cap.plot <- ggplot(state.per.cap.series[date >= "2020-03-10"], aes(x = date, y = deathsNewPerMillion, colour = state)) +
geom_line(aes(group = state)) +
labs(x = "Date",
y = "New COVID-related deaths per million residents",
title = "New COVID-related deaths per million resident (3-day rolling total)",
subtitle = paste0("States with 10 highest counts as of ", format(date.cut, "%B %d, %Y"))) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
state.per.cap.plot
Counties
Confirmed cases
Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.
## county state casesPerMillion rawRanking percentile
## 1: Rockland New York 23,699 1 99
## 2: Westchester New York 19,962 2 99
## 3: Blaine Idaho 19,721 3 99
## 4: Nassau New York 17,358 4 99
## 5: Randolph Georgia 16,377 5 99
## 6: Davidson Tennessee 1,942 144 95
## 7: Richland South Carolina 1,188 318 89
## 8: Pierce Washington 1,035 386 87
## 9: York South Carolina 477 947 69
## 10: Orange California 402 1140 63Our county percentiles over time 
Deaths
Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.
## county state deathsPerMillion rawRanking percentile
## 1: Terrell Georgia 1,172 1 99
## 2: Dougherty Georgia 819 2 99
## 3: Nassau New York 759 3 99
## 4: Randolph Georgia 738 4 99
## 5: Greer Oklahoma 700 5 99
## 6: Richland South Carolina 24 571 81
## 7: Pierce Washington 22 602 80
## 8: Davidson Tennessee 19 668 78
## 9: York South Carolina 11 828 73
## 10: Orange California 6 921 70Our county percentiles over time 
Raw counts
Total confirmed cases
U.S.
agg.cases.US <- state.raw[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.US.plot <- ggplot(agg.cases.US[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = "United States") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.US.plot
Our states
# adjust state name to flow through rest of code (use capitalized full state name)
use.state <- "Washington"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.state.plot <- ggplot(agg.cases.state[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.state.plot
# adjust state name to flow through rest of code (use capitalized full state name)
use.state <- "California"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.state.plot <- ggplot(agg.cases.state[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.state.plot
# adjust state name to flow through rest of code (use capitalized full state name)
use.state <- "South Carolina"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.state.plot <- ggplot(agg.cases.state[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.state.plot
# adjust state name to flow through rest of code (use capitalized full state name)
use.state <- "Tennessee"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.state.plot <- ggplot(agg.cases.state[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.state.plot
Our counties
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "Washington"
use.county <- "Pierce"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.county.plot
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "California"
use.county <- "Orange"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.county.plot
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "South Carolina"
use.county <- "Richland"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.county.plot
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "South Carolina"
use.county <- "York"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.county.plot
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "Tennessee"
use.county <- "Davidson"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
agg.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total confirmed COVID cases",
title = "Total confirmed COVID cases",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.cases.county.plot
New confirmed cases
U.S.
agg.cases.US <- state.raw[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.US
agg.cases.US[, total_cases_prev := temp$total_cases[match(agg.cases.US$date - 1, temp$date)]]
agg.cases.US[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.US.plot <- ggplot(agg.cases.US[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = "United States") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.US.plot
Our states
use.state <- "Washington"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.state
agg.cases.state[, total_cases_prev := temp$total_cases[match(agg.cases.state$date - 1, temp$date)]]
agg.cases.state[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.state.plot <- ggplot(agg.cases.state[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.state.plot
use.state <- "California"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.state
agg.cases.state[, total_cases_prev := temp$total_cases[match(agg.cases.state$date - 1, temp$date)]]
agg.cases.state[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.state.plot <- ggplot(agg.cases.state[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.state.plot
use.state <- "South Carolina"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.state
agg.cases.state[, total_cases_prev := temp$total_cases[match(agg.cases.state$date - 1, temp$date)]]
agg.cases.state[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.state.plot <- ggplot(agg.cases.state[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.state.plot
use.state <- "Tennessee"
cases.state <- state.raw[state %in% use.state]
agg.cases.state <- cases.state[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.state
agg.cases.state[, total_cases_prev := temp$total_cases[match(agg.cases.state$date - 1, temp$date)]]
agg.cases.state[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.state.plot <- ggplot(agg.cases.state[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.state.plot
Our counties
use.state <- "Washington"
use.county <- "Pierce"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.county
agg.cases.county[, total_cases_prev := temp$total_cases[match(agg.cases.county$date - 1, temp$date)]]
agg.cases.county[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.county.plot
use.state <- "California"
use.county <- "Orange"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.county
agg.cases.county[, total_cases_prev := temp$total_cases[match(agg.cases.county$date - 1, temp$date)]]
agg.cases.county[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.county.plot
use.state <- "South Carolina"
use.county <- "Richland"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.county
agg.cases.county[, total_cases_prev := temp$total_cases[match(agg.cases.county$date - 1, temp$date)]]
agg.cases.county[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.county.plot
use.state <- "Tennessee"
use.county <- "Davidson"
cases.county <- county.raw[state %in% use.state & county %in% use.county]
agg.cases.county <- cases.county[order(date),
.(total_cases = sum(cases, na.rm = T)),
by = "date"]
temp <- agg.cases.county
agg.cases.county[, total_cases_prev := temp$total_cases[match(agg.cases.county$date - 1, temp$date)]]
agg.cases.county[, total_new_cases := total_cases - total_cases_prev]
agg.new.cases.county.plot <- ggplot(agg.cases.county[date >= "2020-03-01"], aes(x = date, y = total_new_cases)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Confirmed new COVID cases",
title = "Confirmed new COVID cases by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.cases.county.plot
Total deaths
U.S.
agg.deaths.US <- state.raw[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.US.plot <- ggplot(agg.deaths.US[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = "United States") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.deaths.US.plot
Our states
# adjust state name to flow through rest of code (use capitalized full state name)
use.state <- "Washington"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- cases.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.state.plot <- ggplot(agg.deaths.state[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.state.plot
# adjust state name to flow through rest of code (use capitalized full state name)
use.state <- "California"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- cases.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.state.plot <- ggplot(agg.deaths.state[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.state.plot
# adjust state name to flow through rest of code (use capitalized full state name)
use.state <- "South Carolina"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- cases.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.state.plot <- ggplot(agg.deaths.state[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.state.plot
# adjust state name to flow through rest of code (use capitalized full state name)
use.state <- "Tennessee"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- cases.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.state.plot <- ggplot(agg.deaths.state[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.state.plot
Our counties
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "Washington"
use.county <- "Pierce"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.county.plot
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "California"
use.county <- "Orange"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.county.plot
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "South Carolina"
use.county <- "Richland"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.county.plot
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "South Carolina"
use.county <- "York"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.county.plot
# adjust state and county name to flow through rest of code (use capitalized full state name)
use.state <- "Tennessee"
use.county <- "Davidson"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
agg.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "Total COVID-related deaths",
title = "Total COVID-related deaths",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.deaths.county.plot
New deaths
U.S.
agg.deaths.US <- state.raw[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.US
agg.deaths.US[, total_deaths_prev := temp$total_deaths[match(agg.deaths.US$date - 1, temp$date)]]
agg.deaths.US[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.US.plot <- ggplot(agg.deaths.US[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = "United States") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
scale_y_continuous(labels = comma)
agg.new.deaths.US.plot
Our states
use.state <- "Washington"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- deaths.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.state
agg.deaths.state[, total_deaths_prev := temp$total_deaths[match(agg.deaths.state$date - 1, temp$date)]]
agg.deaths.state[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.state.plot <- ggplot(agg.deaths.state[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.state.plot
use.state <- "California"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- deaths.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.state
agg.deaths.state[, total_deaths_prev := temp$total_deaths[match(agg.deaths.state$date - 1, temp$date)]]
agg.deaths.state[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.state.plot <- ggplot(agg.deaths.state[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.state.plot
use.state <- "South Carolina"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- deaths.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.state
agg.deaths.state[, total_deaths_prev := temp$total_deaths[match(agg.deaths.state$date - 1, temp$date)]]
agg.deaths.state[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.state.plot <- ggplot(agg.deaths.state[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.state.plot
use.state <- "Tennessee"
deaths.state <- state.raw[state %in% use.state]
agg.deaths.state <- deaths.state[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.state
agg.deaths.state[, total_deaths_prev := temp$total_deaths[match(agg.deaths.state$date - 1, temp$date)]]
agg.deaths.state[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.state.plot <- ggplot(agg.deaths.state[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = use.state) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.state.plot
Our counties
use.state <- "Washington"
use.county <- "Pierce"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.county
agg.deaths.county[, total_deaths_prev := temp$total_deaths[match(agg.deaths.county$date - 1, temp$date)]]
agg.deaths.county[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.county.plot
use.state <- "California"
use.county <- "Orange"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.county
agg.deaths.county[, total_deaths_prev := temp$total_deaths[match(agg.deaths.county$date - 1, temp$date)]]
agg.deaths.county[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.county.plot
use.state <- "South Carolina"
use.county <- "Richland"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.county
agg.deaths.county[, total_deaths_prev := temp$total_deaths[match(agg.deaths.county$date - 1, temp$date)]]
agg.deaths.county[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.county.plot
use.state <- "South Carolina"
use.county <- "York"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.county
agg.deaths.county[, total_deaths_prev := temp$total_deaths[match(agg.deaths.county$date - 1, temp$date)]]
agg.deaths.county[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.county.plot
use.state <- "Tennessee"
use.county <- "Davidson"
deaths.county <- county.raw[state %in% use.state & county %in% use.county]
agg.deaths.county <- deaths.county[order(date),
.(total_deaths = sum(deaths, na.rm = T)),
by = "date"]
temp <- agg.deaths.county
agg.deaths.county[, total_deaths_prev := temp$total_deaths[match(agg.deaths.county$date - 1, temp$date)]]
agg.deaths.county[, total_new_deaths := total_deaths - total_deaths_prev]
agg.new.deaths.county.plot <- ggplot(agg.deaths.county[date >= "2020-03-01"], aes(x = date, y = total_new_deaths)) +
geom_bar(stat = "identity") +
labs(x = "Date",
y = "New COVID-related deaths",
title = "New COVID-related deaths by day",
subtitle = paste0(use.county, " County, ", use.state)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
agg.new.deaths.county.plot
Stay-at-home comparisons
- The section is a work in progress – I’ll probably change and add plots over the coming days.
- I’m trying to see what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
- The current plot shows the rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger for the group with orders over time, as more states implement those orders).
- So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
- Over time (if the policies work), what we’d expect is that the line for the group with orders flattens a bit, but we’ll see. It also may become moot if all states issue orders.
- The next plot I want to add will show rate of spread based on how long an order has been in effect, but still thinking about how to do this. Mostly, I’m trying to decide what the right baseline is to compare against.
state.dt <- state.raw
state.dt[, stay_date := stay_dates$start_date[match(state.raw$state, stay_dates$state)]]
state.dt[, population := stay_dates$population[match(state.raw$state, stay_dates$state)]]
#state.dt[, flgOrder := ifelse(is.na(stay_date), 0, 1)]
state.dt[, flgOrder := ifelse(is.na(stay_date) | stay_date >= date, "No", "Yes")]
#state.dt[, ctDaysFromBaseline := ifelse(flgOrder == 0, as.integer(date - as.Date("2020-03-10")),
# as.integer(date - stay_date))]
#state.dt[, ctDaysFromBaseline := as.integer(date - stay_date)]
temp <- state.dt
temp <- temp[, c("date", "state", "cases")]
setnames(temp, "cases", "cases_prev3")
state.dt[, dtPrev3 := date - 3]
state.dt <- merge(state.raw, temp, all.x = T, by.x = c("state", "dtPrev3"), by.y = c("state", "date"))
state.dt[, casesNewPerMillion := as.integer((cases - cases_prev3)/population * 1000000)]
state.agg.comp <- state.dt[date >= "2020-03-10" & !state %in% "Puerto Rico"]
state.agg.comp <- state.agg.comp[order(date, flgOrder),
.(avgNewCases = mean(casesNewPerMillion, na.rm = T),
cts = .N),
by = c("date", "flgOrder")]
state.agg.comp.plot <- ggplot(state.agg.comp, aes(x = date, y = avgNewCases, colour = as.factor(flgOrder))) +
geom_point(aes(size = cts)) +
geom_line(aes(group = flgOrder)) +
labs(x = "Date",
y = "New confirmed COVID cases per million residents",
title = "New COVID cases vs.stay-at-home order",
subtitle = "Average of 3-day rolling totals per million residents",
size = "Count of states",
colour = "Stay-at-home order") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
state.agg.comp.plot